Emergency Management System Research Articles

A Study on Emergency Management Policy Triggered by The Crowd Crush in Itaewon, South Korea

On the evening of October 29, 2022, a large-scale crowd crush killing more than 150 people occurred in Itaewon, South Korea. It aroused the attention of the whole world. To prevent crowd crush and stabilize the social environment, improving existing emergency management policies is necessary. Analyzing the crowd crush in Itaewon, the author finds that due to the impact of the epidemic, people's psychology and behavior when going out in the post-epidemic era are different from those in the early years. Because of the absence of large-scale crowd crush accidents for a long time, people's safety awareness is very weak compared with the early years. And many rumors and bad information about crowd crush appeared on Korean social platforms, which brought bad impact on society and caused secondary injuries to survivors. At the same time, the South Korean government also suffers from problems like loose systems and insufficient prior preparation. Therefore, governments should strengthen the construction of emergency management systems, and refine emergency response plans in advance. Real-time dynamic monitoring can also be adapted to conduct real-time investigation of the flow and the density of people during a large-scale activity, so as to achieve early warning and timely action. It is also necessary to strengthen the construction of the network environment, promptly stop the spread of rumors and bad information, and do a good job of effectively refuting rumors and providing psychological treatment to disaster survivors.

Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China

The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) has become a focus of research in the post-epidemic era. Based on China’s experience in preventing and controlling COVID-19, this paper first analyzes the characteristics and process of ERA in public health emergencies, and then synthesizes the relevant Chinese studies in recent years to identify the intelligent technologies affecting ERA in China using word frequency analysis technology. We also construct an intelligent emergency resource allocation mechanism in four areas: medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence. Finally, we use the entropy-TOPSIS method to evaluate the impact of intelligent technologies on ERA, and we rank the criticality of intelligent technologies. The experimental results show that (i.) medical intelligence and management intelligence are the keys to developing intelligent ERA, and (ii.) among the identified essential intelligent technologies, artificial intelligence (AI), and big data technology have a more significant and critical role in emergency resource intelligence allocation.

IoT-Based Emergency Vehicle Services in Intelligent Transportation System.

Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.

Penerapan Lean Hospital sebagai Upaya Peningkatan Pelayanan Gawat Darurat dalam Cakupan Global

The emergency service unit often has a buildup of patients, which affects services and causes a temporary waiting area for hospitalized patients. The foundation for emergency services is stated in Permenkes 47/2018 with the aim of improving the quality of emergency services that are integrated and integrated in the emergency management system. The purpose of this study is to analyze the application of lean hospital as an effort to improve emergency services in hospitals in a global scope. This research used a literature study method with literature searches, namely Google Scholar and Pubmed, which were selected between 2018-2022. The results of this study were obtained from six research articles related to lean hospital in emergency services that fit the inclusion criteria and keywords. This study shows that lean hospital has a positive influence on the speed of emergency department service time. The reduced time required to provide services can improve the positive image of the hospital. The conclusion of this study is that the implementation of lean hospital can affect waste in waiting time and is most frequent and sensitive. Excessive waiting time can lead to dissatisfaction and a poor view of the hospital by patients or their families.

Smart Ambulances for IoT Based Accident Detection, Tracking and Response

One of the main reasons for death among young people in the current era is based on road accidents. The goal of current research on accident detection systems is to shorten reporting times, increase accident detection accuracy and address them through our modern technology. Our primary goal is to recommend a system that can successfully aid in preventing any kind of accident and if such conditions exist, then how it detects and alerts the relevant authorities and people so that the problem can be handled and addressed swiftly. Automatic mechanisms for detecting traffic accidents must be put in place if assistance is to be given right away. The proposed system model Emergency Request Response and Management System (ERMS) takes the benefit of Vibration sensors (VC) and Accelerometers (AB) through the proposed phases namely AcciDet TracSys and AcciAddr TracSys. Wherein, the AcciDet TracSys helps in detection of the accident, explained in detail in this study and AcciAddr TracSys helps address the people met with an accident through technical ways that are explained in detail in this study, also tracing the location of smart ambulances. With the help of the global positioning system, the accident location is sent to the smart ambulance for timely arrival and is also tracked for further assistance through the internet. Finally, this information is communicated to the main contacts of the accident victim, so that they can reach the hospital on time. Our proposed work meets the need to assist the injured after an accident through the accident detection and addressing system, as just only notifying neighboring ambulances does not cater to the solution to the problem.

Analysis and countermeasures of the “7.20” flood in Zhengzhou

ABSTRACT The “7.20” extreme rainstorm in Zhengzhou caused a serious urban flood. The reasons are as follows: In addition to being affected by global climate change, the rapid urbanization of Zhengzhou in the last 40 years has caused tremendous changes in the city’s water system, underlying surface, urban topography, vertical system, urban underground facilities, all of which will directly or indirectly become factors of urban floods. Then, in response to the increasing frequency of urban floods, it is proposed to develop an overall flood resilience strategy led by the urban stream network; to establish a “large sponge city system of watersheds” and a resilient city’s disaster prevention concept; and to improve the urban flood emergency management system. This is done to adjust measures to local conditions and collaborate with various departments to improve the disaster response capacity of urban floods.

Improvement of China's legal system for public health emergency management from the perspective of lifecycle management

A crucial lesson gained through the pandemic preparedness and response to COVID-19 is that all measures for epidemic control must be law-based. The legal system is related not only to public health emergency management per se but also to all aspects of the institutional supporting system throughout the lifecycle. Based on the lifecycle emergency management model, this article analyses the problems of the current legal system and the potential solutions. It is suggested that the lifecycle emergency management model shall be followed to establish a more comprehensive public health legal system and to gather the intelligence and consensus of experts with different expertise, including epidemiologists, sociologists, economists, jurist and others, which will collaboratively promote the science-based legislation in the field of epidemic preparedness and response for the establishment of a comprehensive legal system for public health emergency management and with Chinese characteristics.

What Has China Learned from Pandemics? The Evolution and Innovation of China’s Pandemic Response and Emergency Management Systems

Public sectors typically learn from crises, providing them the opportunity to improve the performance of crisis management. Through thematic analysis, this study maps out the evolution process of China’s pandemic response and emergency management systems and summarizes the characteristics of China’s crisis learning process, crisis learning subject, and crisis learning content. The findings indicate that China’s pandemic response and emergency management systems have the characteristics of crisis learning with gradual adjustments and continuous innovation. Specifically, under the impetus of China’s political factors, its pandemic response and emergency management systems have been able to learn from crises and have a complete crisis learning process. This crisis learning process includes adaptive learning, as well as single, double, and triple-loop learning. There is also a clear selection preference at various government levels, corresponding crisis learning processes and stages, and the path dependence of crisis learning content. Moreover, political accountability, attention, and pressure are the key factors opening the window of crisis learning, but the decision-making authority is the decisive factor of crisis learning in China’s centralized context. This study provides a theoretical framework for understanding the evolution and changes in the government’s crisis learning model and puts forward policy implications.

Impact of Covid-19 in Health Emergency and Disaster Risk Management System: Healthcare Workforce Management in Covid-19

Introduction:COVID-19 conforms to key baseline characteristics of disaster which is defined as “a situation or event that overwhelms local capacity, necessitating a request for national or international level of assistance.” Many countries faced shortages of health workforce, maldistribution, misalignment of needs and skills of healthcare workers.The research goal is to identify the country responses on the shortage of workforce, their best practices and the lessons learned that may help to better handle any similar crisis in the future.Method:The scoping review was conducted in four electronic academic databases, namely, Medline, Web of Science, EBSCO, and TRIP and 24 scientific articles were reviewed. This study is funded by the World Health Organization Centre for Health Development (WKC-HEDRM-K21001).Results:The main strategies implemented were a financial coordination mechanism, relaxing standards/rule, redeployment, recruiting volunteers, fast tracking medical students, and using other resources in the workforce such as: the recruitment of inactive healthcare workers, returnees whose registration has lapsed within the last 1-2 years and integration of internationally educated health professionals. All these strategies demonstrated advantages like establishing mutual support across nations, organizations, motivating healthcare workers, lessening the workload of healthcare workers, and creating a new staff model for the next pandemic. If a pandemic lasts longer, financial support mechanisms are no longer feasible and longer working hours result in burnout. Managing volunteers, including supervision of their safety and allocation to the area in need, required hard effort and high-level coordination, especially when a needs assessment is unavailable. Another problem was the absence of an available list of resources, including volunteers and retired medical personnel.Conclusion:To date, countries have not yet determined clear policies on how to ensure the sustainability and resilience of the workforce during major health shocks. A follow-up study investigating the strategies implemented is needed.

EMS and Bioterrorism Response

Introduction:Previous studies have found that public health systems within the United States are inadequately prepared for an act of biological terrorism. As the COVID-19 pandemic continues, few studies have evaluated bioterrorism preparedness of Emergency Medical Services (EMS), even in the accelerating environment of biothreats.Method:This study utilized an Internet-based survey to assess the level of preparedness and willingness to respond to a bioterrorism attack and identify factors that predict preparedness and willingness among Nebraska EMS providers. The survey was available for 1 month in 2021 during which 190 EMS providers responded to the survey.Results:Only 56.8% of providers were able to recognize an illness or injury as potentially resulting from exposure to a biological agent. Provider Clinical Competency levels ranged from a low of 13.6% (ability to initiate patient care within his/her professional scope of practice and arrange for prompt referral appropriate to the identified condition(s)) to a high of 74% (the ability to respond to an emergency within the emergency management system of his/her practice, institution, and community). Only 10% of the respondents were both willing and able to effectively function in a bioterror environment.Conclusion:To effectively prepare for and respond to a bioterrorist attack, all levels of the healthcare system need to have the clinical skills, knowledge, and abilities necessary to treat patients exposed to biological agents. Policy changes and increased focus on training and drills are needed to ensure a prepared EMS system, which is crucial to a resilient state. EMS entities need to be aware of the extent of their available workforce so that the country can be prepared for the increasing threat of bioterrorism or other novel emerging infectious disease outbreaks. A resilient nation relies on a prepared set of EMS providers who are willing to respond to biological terrorism events.

A social media event detection framework based on transformers and swarm optimization for public notification of crises and emergency management

Social media allows the spread of vital information regarding crises and emergencies. Thus, emergency management systems can benefit from social media because they can be used to inform the public to take the appropriate precautions. However, social media is riddled with irrelevant information. Therefore, researchers have recently focused on developing robust event detection (ED) systems to extract relevant events and to define their types by relying on deep learning techniques (DL). Hence, this paper proposes an event detection model that merges the DL approach (e.g., MobileBERT) and a novel feature selection (FS) method to improve performance. MobileBERT is a transformer-based model designed to extract features from a text dataset, while the FS is used to preserve the relevant features and to reduce feature representation space. The developed FS method depends on improving the sparrow search algorithm (SSA) using manta ray foraging optimization (MRFO) operators. The modification is conducted to enhance the exploitation ability of the SSA using the operators of MRFO as a local search method. To validate the proposed framework, experiments are conducted using real-world datasets, namely Maven, C6, and C36. The results show the ability of the modified FS method to improve the performance of the proposed framework for ED tasks over other existing methods.

Adequacy of Normative-Legal Framework for Engaging the Military Forces of the Republic of Serbia in Emergency Situations

Engagement of military forces of the Republic of Serbia is determined by the national legislation. The operationalisation of engagement of military forces of the Republic of Serbia is regulated in more detail by strategic-doctrinal documents of the National Security system and rules of use of the Serbian Armed Forces. However, engagement of military forces in peacetime (emergency situations) is specific, from the point of view of ambient conditions for use, i.e. dimensions of the operational environment in which military forces are used. Consequently, clearly defined procedures and methodologies for use of military forces are necessary, which nominally requires precise normative-legal determinants. The existence of adequate normative-legal determinants of engagement of military forces in emergency situations is a problem that is being researched in the paper. The solution to the problem, that is, the goal of the work, is achieved by applying the basic method used in the research is the method of content analysis. A special instrument-sheet for content analysis was constructed for its application. A total of 66 normative-legal documents were examined, which can be divided into four groups: laws; regulations; decrees and decisions; and assessments and instructions. The research, at the level of scientific description, led to the following results: the normative-legal framework of the Republic of Serbia enables the use of military forces in emergency situations; the existing legislation is not adequately regulated, so it is necessary to regulate the use of military forces more closely, with a focus on regulating connections and relations at different levels of coordination of entities and forces of disaster risk reduction and emergency management systems.

Intelligent fire information system based on 3D GIS

BackgroundThis work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things (IoT) and achieve an actual intelligent fire rescue. A smart fire protection information system was designed based on the IoT. A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue. MethodsThe intelligent fire visualization platform based on the three-dimensional (3D) Geographic Information Science (GIS) covers project overview, equipment status, equipment classification, equipment alarm information, alarm classification, alarm statistics, equipment account information, and other modules. The live video accessed through the visual interface can clearly identify the stage of the fire, which facilitates the arrangement of rescue equipment and personnel. The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization: emergency rescue time and the number of vehicles. In addition, an evacuation path optimization method based on the Improved Ant Colony (IAC) algorithm was designed to realize the dynamic optimization of building fire evacuation paths. ResultsThe experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t=17s than the initial value. In addition, the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation, demonstrating that this model could detect fire. ConclusionsThe IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route. Therefore, the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.

Collation of best practices for preparedness: lessons from disasters in Pakistan and Japan

PurposeThis study aims to compare the humanitarian supply chains and logistics of two countries in earthquake preparedness by modifying and using a previously established preparedness evaluation framework.Design/methodology/approachA European flood emergency management system (FEMS) is a seven-dimensional framework to assess a country’s preparedness for flood emergencies. The FEMS framework was modified to apply to earthquakes. Leveraging a multiple explanatory case study approach with data analysis, the authors reconstructed the events of the earthquakes in Pakistan (2005) and Japan (2011) with an applied grading (1–5). Findings were evaluated within the adopted FEMS framework. From a practitioner’s perspective, the framework is applicable and can accelerate support in the field.FindingsPakistan lacked emergency plans before the 2005 earthquake. In contrast, Japan possessed emergency plans before the disaster, helping minimise casualties. Overall, Japan demonstrated considerably better emergency management effectiveness. However, both countries significantly lacked the distribution of responsibilities among actors.Originality/valuePractical factors in the humanitarian supply chain are well understood. However, synthesising individual factors into a comprehensive framework is difficult, which the study solves by applying and adopting the FEMS framework to earthquakes. The developed framework allows practitioners a structured baseline for prioritising measures in the field. Furthermore, this study exemplifies the usefulness of cross-hazard research within emergency management and preparedness in a real-world scenario.

Real-time natural gas explosion modeling of offshore platforms by using deep learning probability approach

Natural gas explosion of offshore platform is prone to cause accidental disaster such as platform collapse and casualties etc. Real-time natural gas explosion consequence reconstruction is essential to support a quick accidental emergency response planning to prevent the accidental escalation to disaster. The widely-used CFD is computationally intensive and thereby has a significant delay. Machine/deep learning-based models offer a potential real-time alternative, which however are not able to quantify the uncertainty of spatial overpressure prediction. This study aims to propose a hybrid deep learning probability model to real-time predict spatial explosion overpressure of offshore platform by using sparsely-observed overpressures. In this hybrid model, Variational Bayesian inference is incorporated into deep learning backbone. Both natural gas explosion experimental and numerical modeling of offshore platform are conducted to construct the benchmark dataset. By using this benchmark dataset, sensitivity analysis of Monte Carlo sampling number N, drop probability p on model's performance is also conducted. The results demonstrated our model exhibits high accuracy with R2 = 0.955 and real-time capability with inference time of 2.9s. Compared to the state-of-the-art model, the additional uncertainty estimation improves the accuracy and robustness of spatial overpressure prediction, which contributes to the reliable explosion accidental emergency decision-making. Overall, this study provides a reliable alternative for constructing digital twin emergency management system to effectively manage natural gas explosion risk of offshore platforms.

Emergency Management Tournament: Ideal Model, Essence and Present Situation of the Incentive

With the continuous improvement of the structure and institution of the emergency management system, the relevant research has become increasingly rich, but the direct subject of emergency management - "people" has not been paid enough attention to; The current normalized political tournament theory is difficult to explain the behavior and choices of " restrictive and safety-ensured tasks" conducted by local officials under emergency management. This article constructs the "people"- based emergency management tournament theory, and makes up the lack of "people" theory in emergency management and the deficiency of political tournament in emergency management field. Firstly, this article constructs an ideal model of emergency management tournament from the concept, conditions and officials' goals in theory. Secondly, according to the different degrees of accountability and incentive, it builds a two-dimensional matrix of four incentive modes, namely "stagnated stalemate", "radical achievement", "conservative fear" and "balanced innovation", and then systematically refines the essence of emergency management tournament theoretically and practically. Thirdly, focusing on practice, it analyzes the incentive effect and limitations of the current emergency management tournament. The reasons for the incentive limitations are that the implementation conditions of the emergency management tournament in the ideal model are not fully met, and the model of "emphasizing accountability and neglecting incentive" in reality. Finally, this article puts forward the countermeasures and suggestions to further improve the emergency management tournament.

Temporary Protection Directive: Testing New Frontiers?

Abstract The activation of the Temporary Protection Directive has been considered a manifestation of solidarity towards refugees that enhances responsibility sharing between the Member States and is, therefore, perceived as a positive development. Focusing on the specific case of Ukrainian refugees, this paper explores the possible implications of the Temporary Protection Directive from the perspective of asylum solidarity. The purpose of the article is twofold. First, to dispel the assumption that the activation of the Directive is a positive step forward in addressing the Ukraine refugee incidence and challenge the presumed benefits of the Directive in terms of asylum solidarity. Second, the article seeks to explore how the detrimental effects of temporary protection can be mitigated both in the short and long term. It proposes possible solutions to prevent a regression in refugee protection standards through temporary protection and examines how a free choice model can be integrated to remedy some of the shortcomings of the current emergency management system.

Assessing the effectiveness of training programme on the competency of medical staffs in public health emergency.

The competencies of medical staff in the public health emergency system and evaluated the effects of system-based professional training were investigated. A competency model for individuals in a public health emergency management system was developed, which contained 33 items with 5 domains. A competency-based intervention was performed. A total of 68 participants from 4 health emergency teams in Xinjiang, China were recruited and randomly divided into 2 groups: the intervention (N = 38) and control groups (N = 30). Participants in the intervention group received competency-based training, while those in the control group received no training. All participants responded to the COVID-19 activities. The competencies of medical staff in the 5 domains were then analyzed in the pre-intervention, post-first training, and post-COVID-19 intervention using a self-designed questionnaire. Participants' competencies were at the middle level at baseline. After the first training, competencies in the 5 domains significantly improved in the intervention group; in the control group, there was a significant increase in professional quality compared in the pre-training. After the response to COVID-19, the mean scores of competencies in the 5 domains significantly increased in both the intervention and control groups compared with those in the post-first training. Psychological resilience scores were higher in the intervention group than in the control group, whereas no significant differences in competencies were found in other domains. Competency-based interventions provided practice and showed a positive effect on improving the competencies of medical staff in public health teams. Med Pr. 2023;74(1):19-26.

A practice-based nursing emergency management system model for public health emergencies: A descriptive qualitative study.

This study aims to explore nursing emergency management system under public health emergencies. A descriptive qualitative study. Semi-structured interviews were conducted during March-November 2020 with 11 nursing emergency management administrators from 11 COVID-19 designated hospitals and infectious disease hospitals under four administrative divisions in China, who were recruited through convenience and purposive sampling. Interviews were audio-recorded and transcribed. Data were analysed with Braun and Clarke's inductive thematic analysis. The study adhered to the Consolidated Criteria for Reporting Qualitative Research. Three main themes emerged from data analysis: (1) Five core elements of nursing emergency management system: Structure, Staff, System, Setting, and Supply (5S); (2) Four phases of nursing emergency management system: Reduction, Readiness, Response and Recovery (4R); and (3) operational process of nursing emergency management system (5S*4R). The findings generated a conceptual model of a nursing emergency management system. This study provides a theoretical basis for nursing emergency management system for public health emergencies in the future. Nurse administrators could refer to this model to either review or develop their system and develop viable interventions to prepare for future public health emergencies.

Comparison of code blue application and results in a training and research hospital before and during the COVID-19 pandemic

Background & Objective: Code blue is an emergency management system that allows for a rapid professional response to the patients of cardiopulmonary arrest (CPA) in hospitals. The time to initiate the call and the response of the ‘Code Blue Team’ may vary in different hospitals, and it me be linked with the survival of the victim. We examined and compared the code blue application utilized in our hospital before and during the COVID-19 pandemic.
 Methodology: Code Blue Call (CBC) logs from March 01, 2018 to March 31, 2022 were retrospectively analyzed. The study period was divided into two parts: March 01, 2018–February 28, 2020 (Group I, pre-pandemic period) and March 01, 2020–March 31, 2022 (Group II, pandemic period).
 Results: During the study period, a total of 1542 CBC’s were received, of which 837 (54.3%) were ‘true’ CBC’s. Of the 837 true CBC’s included in the study, 477 (56.7%) were for male patients and 360 (43.3%) were for the females. We evaluated the month-wise distribution of the CBC’s; the month with the highest number of calls in Group I was January 2019 (n = 29, 17.3%), while in Group II it was December 2020 (n = 59, 23.1%). The arrival time of the code blue team was significantly different between the groups, e.g., 3.15 ± 0.52 vs. 3.81 ± 0.58 min in Group I vs. Group II respectively.
 Conclusion: The intervention times of the code blue team and the success of cardiopulmonary resuscitation were observed to be comparable during the pre-pandemic and pandemic periods. The duration of commencement of intervention is important for the efficacy of cardiopulmonary resuscitation during a pandemic.
 Abbreviations: CBC: Code Blue Call; CBT: code blue team; COVID-19: Coronavirus Disease-19; CPA: cardiopulmonary arrest
 Key words: COVID-19; CPR; Code Blue; Pandemic
 Citation: Çelik HK, Kuşderci HS. Comparison of code blue application and results in a training and research hospital before and during the COVID-19 pandemic. Anaesth. pain intensive care 2022;27(1):123−130. DOI: 10.35975/apic.v27i1.2121
 Received: September 20, 2022; Reviewed: November 30, 2022; Accepted: December 01, 2022